Build · Create · Analysis & Finance
Build a Buyer Persona x Buyer Journey matrix
I get a complete buyer-persona by buyer-journey matrix where every persona and stage combination has exactly one populated cell, so no segment or stage falls through a gap.
You receive: A JSON object { personas: [...], stages: [...], cells: [{ persona, stage, content }] } describing the coverage matrix.
Part of Build a Sales Engine
Opens soon
Cost20 credits
ProtectionHeld until verified delivery
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Example
A sample of what this play produces. Your result is generated for your inputs.
Personas
- Solo Founder
- Early-Stage Product Team Lead
- Ops and Engineering Lead
Stages
- Awareness
- Consideration
- Decision
- Retention
Cells
| Persona | Stage | Content |
|---|---|---|
| Solo Founder | Awareness | Discovers STUD through a developer-community post about paying only once AI-produced work is checked against frozen acceptance criteria, not just promised. The Standard plan cost, $20 a month for 500 credits, reads as within a bootstrapped budget. |
| Solo Founder | Consideration | Compares STUD's per-play credit cost (10 to 40 credits, $0.40 to $1.60 a play) against paying a freelancer directly or doing the deliverable solo, and scans the catalog (about 265 plays across 14 playbooks) for the exact outcome needed. |
| Solo Founder | Decision | Commits to the Standard plan and runs one play as a trial: states the outcome, gets a deliverable, and only spends credits once the delivered artifact passes the frozen acceptance criteria set at intake. |
| Solo Founder | Retention | Returns for a second and third play once the first one settles cleanly, building a habit of commissioning recurring knowledge work instead of producing it solo. |
| Early-Stage Product Team Lead | Awareness | Hears about STUD from a teammate who ran a play for a spec or research brief and got back a deliverable checked against acceptance criteria before payment, not a chat transcript to fact-check by hand. |
| Early-Stage Product Team Lead | Consideration | Weighs whether STUD's frozen intake criteria will hold up for the team's recurring artifacts, and compares the Pro plan (1,500 credits a month) against how many plays the team would actually run. |
| Early-Stage Product Team Lead | Decision | Upgrades the team to the Pro plan and assigns a first batch of plays to a backlog of documentation and analysis work that had been queued behind engineering priorities. |
| Early-Stage Product Team Lead | Retention | Keeps the team on STUD once a monthly cadence of about 12 plays per buyer (illustrative) becomes routine, and expands the play mix as the team's workspace facts accumulate. |
| Ops and Engineering Lead | Awareness | Encounters STUD while researching how to adopt AI agents for internal knowledge work without losing a way to check the output before paying for it. |
| Ops and Engineering Lead | Consideration | Tests STUD's acceptance-criteria model against the team's own quality bar, running one or two plays to see whether the frozen criteria catch the same defects a human reviewer would. |
| Ops and Engineering Lead | Decision | Selects the Ultra plan (5,000 credits a month) to cover a wider concierge capacity, in line with STUD's current illustrative operating limit of one founder-operator handling about 30 plays a week. |
| Ops and Engineering Lead | Retention | Renews month over month, tracking that the team's workspace facts, once entered, keep reaching new plays automatically rather than being re-typed each time. |
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